Wind-Informed Bayesian Classification of L-band SAR Imagery for Sea Ice and Open Water Separation

Abstract. We present a robust, incidence angle (IA) and wind speed aware model for separating sea ice from open water and providing high resolution sea ice concentration (SIC) estimates from L-band synthetic aperture radar (SAR) imagery, with potential climate study and operational benefits. By treating open water as a wind speed-dependent class in an established IA-aware Bayesian maximum likelihood ice type classifier, we account for the wind-driven open water backscatter variability by using external wind information. This method circumvents the need for the extensive training datasets or accurate geophysical model functions (GMFs) typically required by established C-band ice/water separation algorithms. To demonstrate this approach, we utilize wide-swath dual-polarized (HH/HV) L-band SAR data from the ALOS-2 mission. The proposed method provides high accuracy during the challenging melt period, achieving high Matthews Correlation Coefficient (MCC) scores (MCC>0.800), and consistently outperforms passive microwave radiometer (PMW) products during this period. During winter conditions, weak sea ice backscatter and system noise limitations hindered reliable ice/water separation (MCC=0.506). We compare our proposed method to a baseline model without incorporated external wind information, and find that the wind-informed classifier achieves consistently higher classification accuracies. Our results demonstrate that integrating external wind data allows for robust, high resolution ice/water separation in L-band SAR imagery during melting conditions based purely on backscatter intensity, removing the need for computationally heavy textural features.